Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches

Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches

Abstract Background One of the most challenging tasks for bladder cancer diagnosis is to histologically differentiate two early stages, non-invasive Ta and superficially invasive T1, the latter of which is associated with a significantly higher risk of disease progression. Indeed, in a considerable...

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Bibliographic Details
Main Authors: Peng-Nien Yin, Kishan KC, Shishi Wei, Qi Yu, Rui Li, Anne R. Haake, Hiroshi Miyamoto, Feng Cui
Format: Article
Language:English
Published: BMC 2020-07-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Machine learning
Deep learning
Bladder cancer
Histopathology images
Online Access:http://link.springer.com/article/10.1186/s12911-020-01185-z

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http://link.springer.com/article/10.1186/s12911-020-01185-z

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